arXiv:2605.20767v1 Announce Type: cross Abstract: Large language models (LLMs) show potential as simulators of human behavior, offering a scalable way to study responses to interventions. However, because LLMs are trained largely on observational data, interventions in experiments with LLM-simulated synthetic users can induce unintended shifts in latent user attributes, causing user drift where the implicit simulated population differs across treatment conditions, potentially distorting effect estimates. We formalize the confounding or selection bias that can arise due to user drift and show h
The rapid adoption and increasing complexity of LLMs, coupled with their deployment in sensitive applications like human behavior simulation, necessitate a deeper understanding of their inherent biases and limitations.
This research highlights a fundamental flaw in using LLMs for experimental simulation without proper methodological rigor, impacting the reliability of conclusions drawn from such studies.
The perceived validity and generalizability of LLM-simulated experiments are now subject to significant methodological critique, requiring more sophisticated counteractive measures or re-evaluation of their utility.
- · AI researchers specializing in causal inference
- · Developers of robust LLM training methodologies
- · Providers of real-world experimental data
- · Organizations relying solely on LLM simulations for research
- · Researchers overlooking methodological rigor in LLM experiments
- · LLM providers without robust bias mitigation
This paper prompts immediate re-evaluation and methodological refinement for experiments relying on LLM-simulated human behavior.
Increased investment in techniques to de-bias LLMs or better understand their observational limitations for scientific applications will follow.
A potential slow-down in the broad adoption of LLMs for high-stakes social science or policy simulation until these issues are more thoroughly addressed.
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